Gene expression is an important molecular phenotype that bridges the divide between static genomic information and dynamic organismal phenotypes. Therefore, a deeper understanding of the genetic architecture of gene expression variation will have profound consequences for basic and biomedical research. Despite significant progress in characterizing patterns of transcript abundance among individuals and mapping gene expression quantitative trait loci, fundamental questions remain. Specifically, very little is known about the precise mechanisms through which regulatory polymorphisms act. To date, considerable attention has focused on understanding the regulation of gene expression through transcriptional initiation. Although overall gene expression levels are determined through a balance of transcriptional initiation and decay, the regulation of transcript abundance through mRNA decay has received far less attention. In particular, the contribution of heritable variation in mRNA decay rates to gene expression variation has not been systematically explored. The goal of this proposal is to use Saccharomyces cerevisiae as a model system to test the hypothesis that variation in mRNA decay rates constitutes an important source of heritable gene expression variation and to identify causal regulatory polymorphisms that affect mRNA decay rates. To this end, we will measure allele- specific mRNA decay rates in two environmental conditions using massively parallel sequencing in a panel of diploid hybrids constructed from genetically diverse S. cerevisiae strains, whose genome sequences have recently been determined. Furthermore, causal regulatory polymorphisms will be identified by site-directed mutagenesis for 10 genes that exhibit allele-specific mRNA decay rates. The successful completion of this project will provide substantial new insights into a largely unexplored, but potentially important aspect of gene expression variation, the types of regulatory polymorphisms that generate heritable variation in mRNA decay rates, and how environmental perturbations modulate the effects of such polymorphisms. These data will ultimately be a key resource in developing quantitative and predictive models of heritable gene expression variation, which will facilitate interpretations of the deluge of data being generated from next-generation sequencing technology and personal genomics initiatives. Gene expression is the first step in converting DNA sequence information into phenotypes. Many human diseases, such as cancer, arise from improper gene expression. This project will provide critical new insights into how genetic variation influences gene expression levels and ultimately the molecular mechanisms of disease. PUBLIC HEALTH RELEVANCE: Gene expression is the first step in converting DNA sequence information into phenotypes. Many human diseases, such as cancer, arise from improper gene expression. This project will provide critical new insights into how genetic variation influences gene expression levels and ultimately the molecular mechanisms of disease.